AI-driven integration of imaging and radiology language improves stroke mortality prediction.

Background: Quantifying radiologic phenotypes from unstructured text and imaging data may enhance clinical prediction in acute stroke but remains underexplored. This study evaluates the feasibility of automated stroke phenotyping across complementary data sources and assesses whether NLP-derived features from radiology reports improve mortality prediction beyond structured electronic health record (EHR) data. Methods: Two complementary datasets were analyzed. First, MRI lesion masks from a public dataset ( Results: In the MRI reference cohort, mean lesion volume was 3.85 × 10 Conclusion: Automated extraction of stroke phenotypes from both quantitative imaging and radiology text is feasible and reproducible across open datasets. Although MRI lesion volume was not incorporated into mortality models due to dataset limitations, NLP-derived radiologic phenotypes from clinical text provided complementary, interpretable information beyond structured EHR data and modestly improved mortality risk stratification. These findings support the potential of text-derived imaging phenotypes as a scalable and clinically practical enhancement to stroke outcome prediction. Broader validation, incorporation of additional modalities including direct imaging metrics, and workflow-aware implementation strategies will be important next steps toward translating these models into actionable clinical decision support.